Pub Date : 2021-01-01DOI: 10.4018/ijdet.20210101.oa2
Sangeeta Sharma, Arpan Bumb
In the wake of coronavirus pandemic, social distancing became a mandate that led to the transition from traditional classroom-based lectures to computer-based learning. This paper extensively deals with the ranking of the challenges faced by instructors and students. Primary data from 624 participants (399 students and 225 instructors) is collected through a questionnaire. To assign the ranking to the challenges, Technique of Order Preference Similarity to Ideal Solution (TOPSIS) is deployed. A contextual model is developed by using Interpretive Structural Model (ISM) technique that further provides recommendations for prioritizing the challenges that need to be addressed to mitigate the problems faced in online lectures in coronavirus situation. The number of variables is reduced to simplify the interpretation by exploratory factor analysis. The study also provides the basis to formulate the strategies for policymakers and administration after identifying which challenges need to be addressed first for mitigating all the other challenges.
在冠状病毒大流行之后，保持社交距离成为一项任务，导致传统的课堂授课向基于计算机的学习过渡。本文广泛地讨论了教师和学生所面临的挑战的排名。通过问卷调查收集了624名参与者(399名学生和225名教师)的主要数据。为了对挑战进行排序，采用了TOPSIS (Order Preference Similarity To Ideal Solution)方法。利用解释结构模型(ISM)技术开发了一个上下文模型，该模型进一步提供了建议，以确定需要解决的挑战的优先次序，以减轻冠状病毒疫情下在线讲座面临的问题。通过探索性因子分析，减少了变量的数量，简化了解释。该研究还为决策者和行政部门确定需要首先解决哪些挑战以减轻所有其他挑战后制定战略提供了基础。
{"title":"The Challenges Faced in Technology-Driven Classes During COVID-19","authors":"Sangeeta Sharma, Arpan Bumb","doi":"10.4018/ijdet.20210101.oa2","DOIUrl":"https://doi.org/10.4018/ijdet.20210101.oa2","url":null,"abstract":"In the wake of coronavirus pandemic, social distancing became a mandate that led to the transition from traditional classroom-based lectures to computer-based learning. This paper extensively deals with the ranking of the challenges faced by instructors and students. Primary data from 624 participants (399 students and 225 instructors) is collected through a questionnaire. To assign the ranking to the challenges, Technique of Order Preference Similarity to Ideal Solution (TOPSIS) is deployed. A contextual model is developed by using Interpretive Structural Model (ISM) technique that further provides recommendations for prioritizing the challenges that need to be addressed to mitigate the problems faced in online lectures in coronavirus situation. The number of variables is reduced to simplify the interpretation by exploratory factor analysis. The study also provides the basis to formulate the strategies for policymakers and administration after identifying which challenges need to be addressed first for mitigating all the other challenges.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134277593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4018/ijdet.20210101.oa1
Zexuan Chen, J. Jiao, Kexin Hu
Online education has long been suffering from high dropout rate and low achievement. However, both asynchronous and synchronous online instructions have to become effective to serve as a quick response to maintain undisrupted learning during the COVID-19 outbreak. The purpose of the present study was to examine student engagement, learning outcome, and students' perceptions of an online course featured with frequent tasks, quizzes, and tests as formative assessment. Data were collected from the first five weeks of a course that was temporarily converted from blended learning to be fully online in time of school closure. Analysis of students' learning records and scores indicated that students engaged themselves actively in all of the online learning activities and had gained high scores in all tasks, quizzes, and tests. In addition, students held positive perceptions towards the formative assessment.
{"title":"Formative Assessment as an Online Instruction Intervention: Student Engagement, Outcomes, and Perceptions","authors":"Zexuan Chen, J. Jiao, Kexin Hu","doi":"10.4018/ijdet.20210101.oa1","DOIUrl":"https://doi.org/10.4018/ijdet.20210101.oa1","url":null,"abstract":"Online education has long been suffering from high dropout rate and low achievement. However, both asynchronous and synchronous online instructions have to become effective to serve as a quick response to maintain undisrupted learning during the COVID-19 outbreak. The purpose of the present study was to examine student engagement, learning outcome, and students' perceptions of an online course featured with frequent tasks, quizzes, and tests as formative assessment. Data were collected from the first five weeks of a course that was temporarily converted from blended learning to be fully online in time of school closure. Analysis of students' learning records and scores indicated that students engaged themselves actively in all of the online learning activities and had gained high scores in all tasks, quizzes, and tests. In addition, students held positive perceptions towards the formative assessment.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127325497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDET.2020100105
Y. Rajabalee, M. Santally, F. Rennie
This paper reports the findings of a research using marks of students in learning activities of an online module to build a predictive model of performance for the final assessment of the module. The objectives were (1) to compare the performances of students of two cohorts in terms of continuous learning assessment marks and final learning activity marks and (2) to model their final performances from their learning activities forming the continuous assessment using predictive analytics and regression analysis. The findings of this study combined with other findings as reported in the literature demonstrate that the learning design is an important factor to consider with respect to application of learning analytics to improve teaching interventions and students' experiences. Furthermore, to maximise the efficiency of learning analytics in eLearning environments, there is a need to review the way offline activities are to be pedagogically conceived so as to ensure that the engagement of the learner throughout the duration of the activity is effectively monitored.
{"title":"Modeling Students' Performances in Activity-Based E-Learning From a Learning Analytics Perspective: Implications and Relevance for Learning Design","authors":"Y. Rajabalee, M. Santally, F. Rennie","doi":"10.4018/IJDET.2020100105","DOIUrl":"https://doi.org/10.4018/IJDET.2020100105","url":null,"abstract":"This paper reports the findings of a research using marks of students in learning activities of an online module to build a predictive model of performance for the final assessment of the module. The objectives were (1) to compare the performances of students of two cohorts in terms of continuous learning assessment marks and final learning activity marks and (2) to model their final performances from their learning activities forming the continuous assessment using predictive analytics and regression analysis. The findings of this study combined with other findings as reported in the literature demonstrate that the learning design is an important factor to consider with respect to application of learning analytics to improve teaching interventions and students' experiences. Furthermore, to maximise the efficiency of learning analytics in eLearning environments, there is a need to review the way offline activities are to be pedagogically conceived so as to ensure that the engagement of the learner throughout the duration of the activity is effectively monitored.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133375455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDET.2020100106
S. Narayanasamy, Atilla Elçi
Due to tremendous reception on digital learning platforms, many online users tend to register for online courses in MOOC offered by many prestigious universities all over the world and gain a lot on cutting edge technologies in niche courses. As the reception of online courses is increasing on one side, there have been huge dropouts of participants in the online courses causing serious problems for the course owners and other MOOC administrators. Hence, it is deemed necessary to find out the root causes of course dropouts and need to prepare a workable solution to prevent that outcome in the future. In this connection, the authors made use of three machine learning algorithms such as support vector machine, random forest, and conditional random fields. The huge samples of datasets were downloaded from the Open University of China, that is, almost 7K student profiles were extracted for the empirical analysis. The datasets were loaded into a confusion matrix and analyzed for the accuracy, precision, recall, and f-score of the model.
{"title":"An Effective Prediction Model for Online Course Dropout Rate","authors":"S. Narayanasamy, Atilla Elçi","doi":"10.4018/IJDET.2020100106","DOIUrl":"https://doi.org/10.4018/IJDET.2020100106","url":null,"abstract":"Due to tremendous reception on digital learning platforms, many online users tend to register for online courses in MOOC offered by many prestigious universities all over the world and gain a lot on cutting edge technologies in niche courses. As the reception of online courses is increasing on one side, there have been huge dropouts of participants in the online courses causing serious problems for the course owners and other MOOC administrators. Hence, it is deemed necessary to find out the root causes of course dropouts and need to prepare a workable solution to prevent that outcome in the future. In this connection, the authors made use of three machine learning algorithms such as support vector machine, random forest, and conditional random fields. The huge samples of datasets were downloaded from the Open University of China, that is, almost 7K student profiles were extracted for the empirical analysis. The datasets were loaded into a confusion matrix and analyzed for the accuracy, precision, recall, and f-score of the model.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134153211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDET.2020100103
V. Vachharajani, J. Pareek
The demand for higher education keeps on increasing. The invention of information technology and e-learning have, to a large extent, solved the problem of shortage of skilled and qualified teachers. But there is no guarantee that this will ensure the high quality of learning. In spite of large number of students, though the delivery of learning materials and tests to the students have become very easy by uploading the same on the web, assessment could be tedious. There is a need to develop tools and technologies for fully automated assessment. In this paper, an innovative algorithm has been proposed for matching structures of two use-case diagrams drawn by a student and an expert respectively for automatic assessment of the same. Zhang and Shasha's tree edit distance algorithm has been extended for assessing use-case diagrams. Results from 445 students' answers based on 14 different scenarios are analyzed to evaluate the performance of the proposed algorithm. No comparable study has been reported by any other diagram assessing algorithms in the research literature.
{"title":"Effective Structure Matching Algorithm for Automatic Assessment of Use-Case Diagram","authors":"V. Vachharajani, J. Pareek","doi":"10.4018/IJDET.2020100103","DOIUrl":"https://doi.org/10.4018/IJDET.2020100103","url":null,"abstract":"The demand for higher education keeps on increasing. The invention of information technology and e-learning have, to a large extent, solved the problem of shortage of skilled and qualified teachers. But there is no guarantee that this will ensure the high quality of learning. In spite of large number of students, though the delivery of learning materials and tests to the students have become very easy by uploading the same on the web, assessment could be tedious. There is a need to develop tools and technologies for fully automated assessment. In this paper, an innovative algorithm has been proposed for matching structures of two use-case diagrams drawn by a student and an expert respectively for automatic assessment of the same. Zhang and Shasha's tree edit distance algorithm has been extended for assessing use-case diagrams. Results from 445 students' answers based on 14 different scenarios are analyzed to evaluate the performance of the proposed algorithm. No comparable study has been reported by any other diagram assessing algorithms in the research literature.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134451638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDET.2020100101
Márcio Alencar, J. F. Netto
Emotions are part of human life, and they are present on several occasions, like decision making and in social interactions. Computational identification of emotions in texts can be useful in many applications, especially in distance learning courses. This research introduces an animated pedagogic agent, integrated to a Moodle virtual learning environment, with the objective of assisting the tutor in accompanying students, helping the students to acquire knowledge, identifying their emotions, and motivating the student to participate in activities and discussions. As a way of assessing students' emotional state, an experiment was conducted using real data from a completed course, involving students. The results obtained are promising, evidencing the importance of knowing the emotional state of the students, contributing to the learning process.
{"title":"Improving Learning in Virtual Learning Environments Using Affective Pedagogical Agent","authors":"Márcio Alencar, J. F. Netto","doi":"10.4018/IJDET.2020100101","DOIUrl":"https://doi.org/10.4018/IJDET.2020100101","url":null,"abstract":"Emotions are part of human life, and they are present on several occasions, like decision making and in social interactions. Computational identification of emotions in texts can be useful in many applications, especially in distance learning courses. This research introduces an animated pedagogic agent, integrated to a Moodle virtual learning environment, with the objective of assisting the tutor in accompanying students, helping the students to acquire knowledge, identifying their emotions, and motivating the student to participate in activities and discussions. As a way of assessing students' emotional state, an experiment was conducted using real data from a completed course, involving students. The results obtained are promising, evidencing the importance of knowing the emotional state of the students, contributing to the learning process.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"44 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJDET.2020100102
Luis Naito Mendes Bezerra, Márcia Terra da Silva
In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate computational solutions, such as educational data mining (EDM). Previous studies have tested the application of EDM on small datasets. The main contribution of the present study is the application of EDM algorithms and the analysis of the results in a massive course delivered by a Brazilian University to 181,677 undergraduate students enrolled in different fields. The use of key algorithms in educational contexts, such as decision trees and clustering, can reveal relevant knowledge, including the attribute type that most significantly contributes to passing a course and the behavior patterns of groups of students who fail.
{"title":"Educational Data Mining Applied to a Massive Course","authors":"Luis Naito Mendes Bezerra, Márcia Terra da Silva","doi":"10.4018/IJDET.2020100102","DOIUrl":"https://doi.org/10.4018/IJDET.2020100102","url":null,"abstract":"In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate computational solutions, such as educational data mining (EDM). Previous studies have tested the application of EDM on small datasets. The main contribution of the present study is the application of EDM algorithms and the analysis of the results in a massive course delivered by a Brazilian University to 181,677 undergraduate students enrolled in different fields. The use of key algorithms in educational contexts, such as decision trees and clustering, can reveal relevant knowledge, including the attribute type that most significantly contributes to passing a course and the behavior patterns of groups of students who fail.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an online multi-user real-time co-reading (OMURCOR) system to promote the performance of co-reading with collaborative learning. The OMURCOR system utilizes WebSocket to perform synchronization controls on co-reading to allow teachers and students to watch streaming videos together with less delay. Moreover, teachers utilize the system to synchronize control operations of videos on the student site, and the OMURCOR system can be integrated into a learning management system to strengthen students' interaction through co-reading. Unlike traditional platforms, the system supports grouping mechanisms during instruction. A survey was conducted with 104 participants. The bootstrapping square and partial least square approaches of the structural equation modeling (PLS-SEM) are employed via the SmartPLS tool to explore evidence of reliability and validity of the revised TAM. Experimental results reflected that six of the seven hypotheses were supported, and the proposed system has a significant impact on students' learning effectiveness during co-reading.
{"title":"An Online Multi-User Real-Time Seamless Co-Reading System for Collaborative Group Learning","authors":"Chih-Tsan Chang, Cheng-Yu Tsai, Hung-Hsu Tsai, Yuen-Ju Li, Pao-Ta Yu","doi":"10.4018/IJDET.2020100104","DOIUrl":"https://doi.org/10.4018/IJDET.2020100104","url":null,"abstract":"This paper proposes an online multi-user real-time co-reading (OMURCOR) system to promote the performance of co-reading with collaborative learning. The OMURCOR system utilizes WebSocket to perform synchronization controls on co-reading to allow teachers and students to watch streaming videos together with less delay. Moreover, teachers utilize the system to synchronize control operations of videos on the student site, and the OMURCOR system can be integrated into a learning management system to strengthen students' interaction through co-reading. Unlike traditional platforms, the system supports grouping mechanisms during instruction. A survey was conducted with 104 participants. The bootstrapping square and partial least square approaches of the structural equation modeling (PLS-SEM) are employed via the SmartPLS tool to explore evidence of reliability and validity of the revised TAM. Experimental results reflected that six of the seven hypotheses were supported, and the proposed system has a significant impact on students' learning effectiveness during co-reading.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124658378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijdet.2020070101
F. Yang, W. Wu, Y. Wu
The Test-of-English-for-International-Communication (TOEIC) is an important proficiency test for achieving the benchmark of future employment for English language learners worldwide. However, game-based apps for acquiring TOEIC vocabulary have remained scarce. Therefore, an empirical study was conducted to examine the effects of the self-developed the smartphone app Saving Alice for optimizing the acquisition of TOEIC vocabulary and spelling among EFL learners. Multiple sources of data were collected to investigate how Saving Alice affected vocabulary acquisition, including a demographic survey, pre- and post-tests on TOEIC vocabulary, and semi-structured interviews. Both the quantitative and qualitative findings showed that Saving Alice significantly enhanced the student learning outcomes, and that frequency of using game-based mobile apps (GBMAs) correlated with learning outcomes.
托业考试(test of English for international communication, TOEIC)是全球英语学习者实现未来就业基准的重要能力测试。然而，用于获取托业词汇的基于游戏的应用程序仍然很少。因此，我们进行了一项实证研究，以检验自主开发的智能手机应用程序save Alice对优化英语学习者托业词汇和拼写习得的影响。为了研究《拯救爱丽丝》对词汇习得的影响，我们收集了多种来源的数据，包括人口统计调查、托业词汇前和后测试以及半结构化访谈。定量和定性研究结果都表明，《拯救爱丽丝》显著提高了学生的学习成果，使用基于游戏的移动应用程序(gbma)的频率与学习成果相关。
{"title":"Using a Game-Based Mobile App to Enhance Vocabulary Acquisition for English Language Learners","authors":"F. Yang, W. Wu, Y. Wu","doi":"10.4018/ijdet.2020070101","DOIUrl":"https://doi.org/10.4018/ijdet.2020070101","url":null,"abstract":"The Test-of-English-for-International-Communication (TOEIC) is an important proficiency test for achieving the benchmark of future employment for English language learners worldwide. However, game-based apps for acquiring TOEIC vocabulary have remained scarce. Therefore, an empirical study was conducted to examine the effects of the self-developed the smartphone app Saving Alice for optimizing the acquisition of TOEIC vocabulary and spelling among EFL learners. Multiple sources of data were collected to investigate how Saving Alice affected vocabulary acquisition, including a demographic survey, pre- and post-tests on TOEIC vocabulary, and semi-structured interviews. Both the quantitative and qualitative findings showed that Saving Alice significantly enhanced the student learning outcomes, and that frequency of using game-based mobile apps (GBMAs) correlated with learning outcomes.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130306009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijdet.2020070103
Izzul Fatawi, I. Degeng, P. Setyosari, S. Ulfa, T. Hirashima
One of the success factors in online learning is student engagement. Therefore, the use of technology to influence student engagement in meaningful and effective learning experiences is worthy for investigation. Concept mapping is an effective knowledge construction strategy to help students. This study investigates the influence of concept maps as a formative assessment of online learning and its impact on student engagement and learning outcomes. The design of experiment used the non-equivalent comparison group pretest-posttest. It was included in the quasi-experiment to compare two different groups. The results reveal strong evidence that concept mapping not only improves learning outcomes, but also increases student engagement in all types of tested engagements, namely behavioral, emotional, and cognitive.
{"title":"Effect of Online-Based Concept Map on Student Engagement and Learning Outcome","authors":"Izzul Fatawi, I. Degeng, P. Setyosari, S. Ulfa, T. Hirashima","doi":"10.4018/ijdet.2020070103","DOIUrl":"https://doi.org/10.4018/ijdet.2020070103","url":null,"abstract":"One of the success factors in online learning is student engagement. Therefore, the use of technology to influence student engagement in meaningful and effective learning experiences is worthy for investigation. Concept mapping is an effective knowledge construction strategy to help students. This study investigates the influence of concept maps as a formative assessment of online learning and its impact on student engagement and learning outcomes. The design of experiment used the non-equivalent comparison group pretest-posttest. It was included in the quasi-experiment to compare two different groups. The results reveal strong evidence that concept mapping not only improves learning outcomes, but also increases student engagement in all types of tested engagements, namely behavioral, emotional, and cognitive.","PeriodicalId":298910,"journal":{"name":"Int. J. Distance Educ. Technol.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131749094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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